Quantifying smartphone ‘use’: Choice of measurement impacts relationships between ‘usage’ and health

May combine two claims.

In regression models, only smartphone addiction scores predicted mental health outcomes, whereas objective logs or estimates were not significant predictors.

“Further, in regression models, only smartphone”

• AssumptionRests on an assumption: Objective smartphone logs from Activity Logger and Apple Screen Time accurately reflect participants' actual smartphone use.

May combine two claims.

Anxiety, depression, and stress had significant positive correlations with smartphone addiction scores (all p < .01), which did not occur with any other smartphone measure.

“Notably anxiety, depression, and stress had significant positive correlations with smartphone addiction scores (all p < .01),which did not occur with any other smartphone measure (see Figure 1 for objective screen time specifically).”

• AssumptionRests on an assumption: Objective smartphone logs from Activity Logger and Apple Screen Time accurately reflect participants' actual smartphone use.

May combine two claims.

Smartphone addiction scores generated rs equal to or larger than .39 with mental health variables, whereas estimates and objective variables were lower (all rs < .2).

“In terms of effect sizes, smartphone addiction scores generated r s equal to or larger than .39 with mental health variables, whereby estimates and objective variables were lower (all r s < .2) (see Table 2; Figure 3).”

• AssumptionRests on an assumption: Objective smartphone logs from Activity Logger and Apple Screen Time accurately reflect participants' actual smartphone use.

May combine two claims.

For anxiety, depression, and stress, associations with smartphone addiction (PSU) were significantly higher than associations with estimates and objective logs (all p < .05).

“Findings showed that when assessing relationships with anxiety, depression, and stress that associations with smartphone addiction (PSU) were all significantly higher than the associations with estimates and objective logs (all p < .05)(see Table 6).”

May combine two claims.

Median daily screen time had near-zero correlations with body fat percentage (rs = -.01) and skeletal muscle mass percentage (rs = .06).

“Scatter Plots Illustrating Linear Relationships Between Median Daily Screen Time (Hours) and Six Health Variables; Body Mass Index (r s = -.32),Body Fat Percentage (r s = -.01),Skeletal Muscle Mass Percentage (r s = .06),Depression (r s = .05),Anxiety (r s = .00),and Stress (r s = .00).”

• AssumptionRests on an assumption: Objective smartphone logs from Activity Logger and Apple Screen Time accurately reflect participants' actual smartphone use.

May combine two claims.

There was a significant difference in effect sizes for mental health associations between estimated and objective measures of pickups, with correlations running in opposite directions (all p < .05).

“However, there was a significant difference in effect sizes for mental health associations depending on whether an estimated or objective measure of pickups was used, with correlations running in the opposite direction (all p < .05)(see Table 6 and Figure 3).”

May combine two claims.

Median daily screen time had near-zero correlations with depression (rs = .05), anxiety (rs = .00), and stress (rs = .00).

“Scatter Plots Illustrating Linear Relationships Between Median Daily Screen Time (Hours) and Six Health Variables; Body Mass Index (r s = -.32),Body Fat Percentage (r s = -.01),Skeletal Muscle Mass Percentage (r s = .06),Depression (r s = .05),Anxiety (r s = .00),and Stress (r s = .00).”

• AssumptionRests on an assumption: Objective smartphone logs from Activity Logger and Apple Screen Time accurately reflect participants' actual smartphone use.

May combine two claims.

Neither daily smartphone screen time nor pickups suggested the presence of any adverse effects on BMI or body fat percentage.

“Nevertheless, neither suggested the presence of any adverse effects between daily smartphone screen time and pickups on these measures of physical health.”

• AssumptionRests on an assumption: Objective smartphone logs from Activity Logger and Apple Screen Time accurately reflect participants' actual smartphone use.

May combine two claims.

In Study 2, smartphone addiction scores correlated with mental health variables at r_s ≥ .36, while estimates and objective variables had correlations r_s ≤ .21.

“Mirroring Study 1, smartphone addiction scores consistently had effect sizes that were at least .36 or larger when correlated with mental health variables.Estimates and objective variables were lower (all r s ≤ .21)(see Figure 3 or Table 5).”

May combine two claims.

High-risk and low-risk groups did not significantly differ in average daily screen time (W = 3357, p = .39) or average daily pickups (W = 3216, p = .70).

“Wilcoxon rank sum tests showed that the two groups did not significantly differ in their amounts of average daily screen time (W = 3357, p = .39)or average daily pickups (W = 3216, p = .70).”

• AssumptionRests on an assumption: iOS Screen Time data accurately reflect actual smartphone use.

May combine two claims.

High-risk and low-risk groups did not significantly differ in estimated daily screen time (W = 3489.5, p = .19) or estimated daily pickups (W = 2721, p = .20).

“This was mirrored when exploring differences in estimated daily screen time (W = 3489.5,p = .19)and estimated daily pickups (W = 2721, p = .20).”

May combine two claims.

In statistical models, average daily pickups and average daily screen time did not significantly predict anxiety, depression, or stress, and explained less than 2% of the variance.

“This was further supported with statistical models, which demonstrated that average daily pickups and average daily screen time did not significantly predict anxiety, depression, or stress, and explained less than 2% of the variance.”

• AssumptionRests on an assumption: iOS Screen Time data accurately reflect actual smartphone use.

May combine two claims.

Objective smartphone use (daily pickups and screen time) had correlations r_s ≤ .17 with mental health symptomatology (anxiety, depression, and stress), whereas PSU scale had r_s ≥ .36.

“Specifically, r s ≤ .17were repeatedly found between objective smartphone use (daily pickups and screen time) and mental health symptomatology (anxiety, depression, and stress), whereas larger effects were observed when relying on a problematic use scale (all r s ≥ .36).”

• AssumptionRests on an assumption: iOS Screen Time data accurately reflect actual smartphone use.

May combine two claims.

The size of the relationship was fourfold in Study 1 and almost three times as large in Study 2.

“Notably, the size of the relationship was fourfold in Study 1, and almost three times as large in Study 2.”

May combine two claims.

These relationships are nearly thrice in a large sample and fourfold in a small sample.

“These are nearly thrice in a large sample and fourfold in a small sample.”

• AssumptionRests on an assumption: iOS Screen Time data accurately reflect actual smartphone use.

The quote only partly states this claim.

In Study 2, smartphone addiction was positively correlated with anxiety (rs = .43, 95% CI [0.31, 0.54], p < .001).

“Anxiety | .43 *** | 0.31, 0.54”

The quote only partly states this claim.

In linear regression models, smartphone addiction significantly predicted stress (B = 0.12, p < .001).

“Smartphone addiction 0.10 *** 0.11 *** 0.12 *** -0.03 -25.22 * -0.02”

Probably not a substantial claim: may not pass S3 (load-bearing).

In Study 2, pickups estimate was positively correlated with average daily pickups (rs = .30, 95% CI [.16, .42], p < .001).

“Pickups estimate | .05 | -.10, .19 | .15 * | .01, .29 | | | .10 | -.04, .24 | .30 *** | .16, .42”

Probably not a substantial claim: may not pass S2 (checkable).

Addressing people's appraisals including worries about their technology usage is likely to have greater mental health benefits than reducing their overall smartphone use.

“We conclude that addressing people's appraisals including worries about their technology usage is likely to have greater mental health benefits than reducing their overall smartphone use.”

• AssumptionRests on an assumption: Objective smartphone logs from Activity Logger and Apple Screen Time accurately reflect participants' actual smartphone use.

Classified as probably not a substantial claim.

Survey research has repeatedly linked increased smartphone screen time to lower psychological well-being.

“Survey research has repeatedly linked increased smartphone screen time to lower psychological well-being (Twenge et al., 2018).”

• cites Twenge et al., 2018Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

90.9% of users report that they typically are sitting when using their smartphone.

“Accordingly, people report that 87% of all phone use occurs while seated (Barkley & Lepp, 2016), and similarily, 90.9% of users report that they typically are sitting when using their smartphone (Xiang et al., 2020).”

• cites Xiang et al., 2020Not checked yet

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Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing). May combine two claims.

Increased smartphone use lowers energy expenditure due to sedentary behaviors, and this mechanism results in greater body fat and higher rates of obesity.

“Thus, it has been proposed that increased smartphone use lowers energy expenditure due to sedentary behaviors, and it is this mechanism, which results in greater body fat and higher rates of obesity (Hamilton et al., 2007;Kim et al., 2015).”

• cites Hamilton et al., 2007Not checked yet

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• cites Kim et al., 2015Not checked yet

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May combine two claims.

When using problematic smartphone use scales, research consistently links higher scores with greater mental health symptomology; however, these relationships seem to either dissipate or lessen when collecting duration estimates of use or objective logs.

“Specifically, when using problematic smartphone use scales, research consistently links higher scores with greater mental health symptomology; however, these relationships seem to either dissipate or lessen when collecting the duration estimates of use or objective logs (Elhai et al., 2017;Harwood et al., 2014;Katevas et al., 2018;Rozgonjuk et al., 2018;Vahedi & Saiphoo, 2018).”

• cites Elhai et al., 2017Not checked yet

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• cites Harwood et al., 2014Not checked yet

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• cites Katevas et al., 2018Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

People report that 87% of all phone use occurs while seated.

“Accordingly, people report that 87% of all phone use occurs while seated (Barkley & Lepp, 2016), and similarily, 90.9% of users report that they typically are sitting when using their smartphone (Xiang et al., 2020).”

• cites Barkley & Lepp, 2016Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Concerns regarding overuse have led to an abundance of usage scales being created to measure new constructs, including addiction, nomophobia, and problematic use.

“Moreover, in recent years, concerns regarding”

• cites Ellis, 2019Not checked yet

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• cites Thomée, 2018Not checked yet

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May combine two claims.

How users think about and appraise their own smartphone usage is uniquely related to well-being and can be considered separately from objective use of the device itself.

“When documenting links between smartphone use and health, nuanced approaches suggest that how users think about and appraise their own smartphone usage is uniquely related to well-being and can be considered separately from objective use of the device itself.”

May combine two claims.

Research linking physical activity or sedentary behavior to smartphone use is scarce and yet to be examined precisely using objective logs.

“Therefore, research linking physical activity or sedentary behavior to smartphone use is also scarce and yet to be examined precisely using objective logs.”

May combine two claims.

Although 9 out of 14 articles in a recent systematic review showed a negative relationship between smartphone use and physical activity, none of the articles measured smartphone use objectively via logs from the device itself.

“However, although 9 out of 14 articles in a recent systematic review showed a negative relationship between smartphone use and physical activity, none of the articles measured smartphone use objectively via logs from the device itself (Zagalaz-Sánchez et al., 2019).”

• cites Zagalaz-Sánchez et al., 2019Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Defining or measuring PSU in relation to overuse or excessive use has foundations in the Behavioral Addictions framework, where tolerance is a key component.

“This has foundations in the Behavioral Addictions framework, where tolerance is a key component (e.g., the need to increase use over time to get the same”

• cites Billieux, Maurage et al., 2015Not checked yet

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• cites Elhai et al., 2017Not checked yet

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• cites Kim, 2017Not checked yet

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May combine two claims.

Agreeing with statements such as using my smartphone longer than I had intended and having tried time and again to shorten my smartphone use time but failing all the time only shows that a person is negatively appraising their smartphone use, and is not a measure of frequency or screen time in itself.

“However, agreeing with these statements only shows that a person is negatively appraising their smartphone use, and is not a measure of frequency or screen time in itself.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Study 1 sample size deviated from the preregistered sample size of 84 due to laboratory access and technical issues.

“This deviates from our preregistered sample size of 84 due to laboratory access and technical issues.”

Classified as probably not a substantial claim. May combine two claims.

The smartphone addiction scale (SAS) was chosen because it is widely cited and correlates highly with a variety of other PSU measures, which all appear to measure the same construct.

“This scale was chosen because it is widely cited and correlates highly with a variety of other PSU measures, which all appear to measure the same construct (Davidson et al., 2020;Ellis et al., 2019;Thomée, 2018).”

• cites Davidson et al., 2020Not checked yet

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• cites Ellis et al., 2019Not checked yet

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• cites Thomée, 2018Not checked yet

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Probably not a substantial claim: may not pass S2 (checkable).

People's appraisals of their smartphone use and actual usage should be considered separately.

“This evidence already suggests that people's appraisals of their smartphone use and actual usage should be considered seperately.”

Classified as probably not a substantial claim.

If negative associations with physical and mental health are driven entirely by usage appraisals, then providing interventions that focus on usage behaviors alone may not deliver any benefits.

“For example, if negative associations with physical and mental health are driven entirely by usage appraisals, then providing interventions that focus on usage behaviors alone may not deliver any benefits (Loid et al., 2020).”

• cites Loid et al., 2020Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Bioelectrical impedance assessment using the Tanita MC-780MA was a good alternate to magnetic resonance imaging and dual-energy X-ray absorptiometry (DEXA), which are costly and time consuming.

“Bioelectrical impedance assessment using the Tanita MC-780MA was a good alternate to magnetic resonance imaging and dual-energy X-ray absorptiometry (DEXA) which are costly and time consuming (Verney et al., 2015).”

• cites Verney et al., 2015Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Using >10 as a cutoff point, the GAD-7 has been shown to have 89% sensitivity and 82% specificity with a diagnosis of general anxiety disorder.

“Using >10 as a cutoff point, the GAD-7 has been shown to have 89% sensitivity and 82% specificity with a diagnosis of general anxiety disorder (Kroenke et al., 2007).”

• cites Kroenke et al., 2007Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Due to skew, Spearman rank order correlations with Fieller et al. (1957) variance were conducted when calculating 95% CI because these are robust against non-normality.

“Due to this skew, we followed Bishara and Hittner (2017) recommendations and conducted Spearman rank order correlations with Fieller et al. (1957) variance when calculating 95% CI as these are robust against non-normality.”

• cites Bishara and Hittner (2017)Not checked yet

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• cites Fieller et al. (1957)Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

The Tanita MC-780MA produces body fat assessments which highly correlate with DEXA assessment (r = .85), providing concurrent validity.

“Notably, the Tanita MC-780MA produces body fat assessments which highly correlate with DEXA assessment (r = .85)providing concurrent validity (Verney et al., 2015).”

• cites Verney et al., 2015Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

A priori power calculation showed that during two-tailed analysis a sample size of 192 participants was enough to detect small effect sizes of rs >= .2 with a power of .8 when alpha = .05.

“A priori power calculation was performed which showed during two-tailed analysis a sample size of 192 participants was enough to detect small effect sizes of r s ≥ .2 with a power of .8 when α = .05.”

May combine two claims.

Both overall scores derived from the SAS and individual items have latent relationships with stress and depression scales (but not with objective smartphone measures).

“Second, both overall scores derived from the SAS and individual items have latent relationships with stress and depression scales (but not with objective smartphone measures) (Davidson et al., 2020).”

• cites Davidson et al., 2020Not checked yet

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May combine two claims.

The same review concluded that intense or frequent mobile use was associated with greater mental health symptomatology, but this conclusion was based primarily on findings derived from PSU scales.

“The same review concluded that intense or frequent mobile use was associated with greater mental health symptomatology, yet this conclusion was based primairly on findings derived from PSU scales.”

• cites Thomée, 2018Not checked yet

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Probably not a substantial claim: may not pass S2 (checkable).

Helping people manage their appraisals of use (e.g., worries) is more likely to provide a benefit to well-being than reducing use of the device itself.

“Our findings alternatively suggest that helping people manage their appraisals of use (e.g., worries) is more likely to provide a benefit to well-being than reducing use of the device itself.”

May combine two claims.

The size of the relationship between estimated screen time and actual screen time is larger in Study 2 than previous work, and may explain why association between mental health and these two measures of usage did not significantly differ.

“The size of the relationship between estimated screen time and actual screen time is larger in Study 2 than previous work, and may explain why association between mental health and these two measures of usage did not significantly differ (Andrews et al., 2015;Ellis et al., 2019).”

• cites Andrews et al., 2015Not checked yet

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• cites Ellis et al., 2019Not checked yet

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May combine two claims.

By using a quasi-experimental approach in the exploratory analysis of Study 2 and analyzing naturally occurring levels of mental health symptomatology, our findings cast doubt on the presence of any causal relationships proposed previously, as those in a high symptomatology group did not have increased general smartphone usage.

“However, by using a quasi-experimental approach in the exploratory analysis of Study 2 and through analyzing the naturally occurring levels of mental health symptomatology in our sample, our findings cast doubt on the presence of any causal relationships that have been proposed previously, as those in a high symptomatology group did not have increased general smartphone usage.”

May combine two claims.

BMI scores in Study 1 had large correlations with body fat percentage (r_s = .70) and skeletal muscle mass percentage (r_s = -.73), so BMI was accepted as a relatively good proxy in Study 2.

“Nonetheless, as BMI scores in Study 1 had large correlations with body fat percentage (r s = .70)and skeletal muscle mass percentage (r s = -.73),we accepted this as a relatively good proxy in Study 2.”

• AssumptionRests on an assumption: Participants' self-reported height and weight are accurate enough to compute BMI.

May combine two claims.

By moving Study 2 to an online platform, we achieved a larger and more representative sample.

“In addition, by moving our second study to an online platform, we achieved a larger and more representative sample.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The hypotheses primarily focus on effect sizes rather than just statistical significance.

“our hypotheses primarily focus on effect sizes rather than just statistical significance following reviewer recommendations.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Preregistered analyses have been reported in full.

“While preregistered analyses have been reported in full”

Probably not a substantial claim: may not pass S3 (load-bearing).

References to the displacement and goldilocks hypotheses were removed from the design.

“References to both of these have now been removed.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The original design and analysis plans for the study were preregistered.

“Our original design and analysis plans were preregistered: https://osf.io/ 5g9v6.”

Classified as probably not a substantial claim. May combine two claims.

Our findings favour addressing people's appraisals about their usage rather than reducing their overall screen time, as the former relates more strongly to mental health symptomatology.

“Finally, our findings would favour addressing peoples' appraisals about their usage rather than reducing their overall screen time, as the former relates more strongly to mental health symptomatology.”

Classified as probably not a substantial claim.

Median daily screen time was calculated instead of average daily screen time to control for long 'ON' durations.

“median daily screen time was calculated instead of average daily screen time to control for any long”

Probably not a substantial claim: may not pass S3 (load-bearing).

Following reviewer advice, several deviations from the preregistered design and analysis plan were made.

“However, following advice from reviewers, several deviations can be summarised as follows.”

Classified as probably not a substantial claim.

The study compared six well-being variables with smartphone use rather than the 18 collected in Study 1.

“we only compared six well-being variables with smartphone use throughout rather than 18 collected as part of Study 1”

Classified as probably not a substantial claim.

The study reported daily pickups instead of daily checks.

“we now report daily pickups (any smartphone use) instead of daily checks (uses under 15 s)”